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Improve interaction plot groupings

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This commit is contained in:
Bryan Roessler
2024-10-02 11:02:40 -04:00
parent ce32452920
commit 9769bae3d1
1 changed files with 44 additions and 34 deletions
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78
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -340,11 +340,8 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats, group_vars, ove
Z_Shift_L = first(Z_Shift_L),
Z_Shift_K = first(Z_Shift_K),
Z_Shift_r = first(Z_Shift_r),
Z_Shift_AUC = first(Z_Shift_AUC),
Z_Shift_AUC = first(Z_Shift_AUC)
NG = first(NG),
DB = first(DB),
SM = first(SM)
) %>%
arrange(desc(Z_lm_L), desc(NG)) %>%
ungroup() %>%
@@ -669,16 +666,14 @@ generate_plate_analysis_plot_configs <- function(variables, df_before = NULL, df
generate_interaction_plot_configs <- function(df, type) {
# Set group_vars based on the type (reference or deletion)
if (type == "reference") {
group_vars <- c("OrfRep", "Gene", "num")
df <- df %>%
mutate(OrfRepCombined = paste(!!!syms(group_vars), sep = "_"))
} else if (type == "deletion") {
group_vars <- c("OrfRep", "Gene")
df <- df %>%
mutate(OrfRepCombined = OrfRep)
}
# Define the limits for the plots
limits_map <- list(
L = c(0, 130),
K = c(-20, 160),
@@ -696,14 +691,18 @@ generate_interaction_plot_configs <- function(df, type) {
overall_plot_configs <- list()
delta_plot_configs <- list()
# Overall plots with lm_line for each interaction
# Overall statistics plots
for (var in names(limits_map)) {
y_limits <- limits_map[[var]]
# Use the pre-calculated lm intercept and slope from the dataframe
lm_intercept_col <- paste0("lm_intercept_", var)
lm_slope_col <- paste0("lm_slope_", var)
# Ensure no NA or invalid values in lm_line calculations
intercept_value <- mean(df[[lm_intercept_col]], na.rm = TRUE)
slope_value <- mean(df[[lm_slope_col]], na.rm = TRUE)
plot_config <- list(
df = df,
plot_type = "scatter",
@@ -716,39 +715,47 @@ generate_interaction_plot_configs <- function(df, type) {
x_breaks = unique(df$conc_num_factor_factor),
x_labels = as.character(unique(df$conc_num)),
position = "jitter",
smooth = TRUE,
smooth = TRUE,
lm_line = list(
intercept = mean(df[[lm_intercept_col]], na.rm = TRUE),
slope = mean(df[[lm_slope_col]], na.rm = TRUE)
intercept = intercept_value,
slope = slope_value
)
)
overall_plot_configs <- append(overall_plot_configs, list(plot_config))
}
# Delta plots (add lm_line if necessary)
unique_groups <- df %>% select(all_of(group_vars)) %>% distinct()
# Delta interaction plots
grouped_data <- df %>%
group_by(across(all_of(group_vars))) %>%
group_split()
for (i in seq_len(nrow(unique_groups))) {
group <- unique_groups[i, ]
group_data <- df %>% semi_join(group, by = group_vars)
for (group_data in grouped_data) {
OrfRep <- first(group_data$OrfRep)
Gene <- first(group_data$Gene)
num <- if ("num" %in% names(group_data)) first(group_data$num) else ""
OrfRep <- as.character(group$OrfRep)
Gene <- if ("Gene" %in% names(group)) as.character(group$Gene) else ""
num <- if ("num" %in% names(group)) as.character(group$num) else ""
if (type == "reference") {
OrfRepTitle <- paste(OrfRep, Gene, num, sep = "_")
} else if (type == "deletion") {
OrfRepTitle <- OrfRep
}
for (var in names(delta_limits_map)) {
y_limits <- delta_limits_map[[var]]
y_span <- y_limits[2] - y_limits[1]
# For error bars
WT_sd_value <- group_data[[paste0("WT_sd_", var)]][1]
# Error bars
WT_sd_value <- first(group_data[[paste0("WT_sd_", var)]], default = 0)
Z_Shift_value <- round(group_data[[paste0("Z_Shift_", var)]][1], 2)
Z_lm_value <- round(group_data[[paste0("Z_lm_", var)]][1], 2)
R_squared_value <- round(group_data[[paste0("R_squared_", var)]][1], 2)
NG_value <- group_data$NG[1]
DB_value <- group_data$DB[1]
SM_value <- group_data$SM[1]
# Z_Shift and lm values
Z_Shift_value <- round(first(group_data[[paste0("Z_Shift_", var)]], default = 0), 2)
Z_lm_value <- round(first(group_data[[paste0("Z_lm_", var)]], default = 0), 2)
R_squared_value <- round(first(group_data[[paste0("R_squared_", var)]], default = 0), 2)
# NG, DB, SM values
NG_value <- first(group_data$NG, default = 0)
DB_value <- first(group_data$DB, default = 0)
SM_value <- first(group_data$SM, default = 0)
annotations <- list(
list(x = 1, y = y_limits[2] - 0.2 * y_span, label = paste("ZShift =", Z_Shift_value)),
@@ -759,14 +766,17 @@ generate_interaction_plot_configs <- function(df, type) {
list(x = 1, y = y_limits[1], label = paste("SM =", SM_value))
)
# Delta plot configuration with lm_line if needed
# lm_line for delta plots
lm_intercept_value <- first(group_data[[lm_intercept_col]], default = 0)
lm_slope_value <- first(group_data[[lm_slope_col]], default = 0)
plot_config <- list(
df = group_data,
plot_type = "scatter",
x_var = "conc_num_factor_factor",
y_var = var,
x_label = unique(group_data$Drug)[1],
title = paste(OrfRepCombined, Gene, sep = " "),
title = paste(OrfRepTitle, Gene, num, sep = " "),
coord_cartesian = y_limits,
annotations = annotations,
error_bar = TRUE,
@@ -779,15 +789,15 @@ generate_interaction_plot_configs <- function(df, type) {
x_labels = as.character(unique(group_data$conc_num)),
ylim_vals = y_limits,
lm_line = list(
intercept = group_data[[lm_intercept_col]][1],
slope = group_data[[lm_slope_col]][1]
intercept = lm_intercept_value,
slope = lm_slope_value
)
)
delta_plot_configs <- append(delta_plot_configs, list(plot_config))
}
}
# Calculate dynamic grid layout based on the number of plots for the delta_L plots
# Calculate dynamic grid layout
grid_ncol <- 4
num_plots <- length(delta_plot_configs)
grid_nrow <- ceiling(num_plots / grid_ncol)